The 2025 SLAS Innovation Award Winner and Finalists Announced

The Society for Laboratory Automation and Screening (SLAS) Innovation Award is an annual cash prize (US$ 10,000) given out at the SLAS International Conference and Exhibition. The award goes to one exceptional podium presentation that showcases the most innovative research for improving laboratory technology. 

The SLAS 2025 Innovation Award winner is Taci Pereira, the CEO of Systemic Bio. Her work is addressing challenges in generating scalable, human-relevant data, as traditional animal models and simplistic in vitro systems often fail to accurately mimic human biology. The h-VIOS™ platform addresses these issues by integrating bioprinted organ-chips with endothelialized vasculatures, enabling complex, human-like tissue models and multimodal data generation to assess therapeutic safety and efficacy in applications like drug-induced liver and vascular injuries. “h-VIOS: A Human-Relevant Drug Discovery and Development Platform Using Bioprinted Human Tissues.”

The other nine finalists were:

• S. Tori Ellison, NCATS, has developed a 3D bioprinted skin tissue model that mimics the dermis and epidermis to study HSV infection pathways and test new therapeutics. Using fluorescent biosensors and a high-throughput format, over 700 compounds were screened, demonstrating that the potency of antiviral treatments depends on the cell type and mechanism of action, offering a scalable solution for advancing HSV drug discovery. “A 3D Bioprinted Skin Assay Platform to Perform High-Throughout Screens and Identify Potent HSV Anitvirals.” 
• Keisuke Goda, University of Tokyo, has overcome the limitation of traditional cell sorting methods like FACS with image-activated cell sorting (IACS). IACS uses real-time, AI-powered imaging to sort live cells based on visual and functional attributes, with applications in fields ranging from cancer biology to food science. “Intelligent Image-Activated Cell Sorting & Beyond.”
• Sunghoon Kwon, Seoul National University, explores the role of spatial technologies in oncology, highlights recent discoveries, and offers perspectives on therapeutic applications and future innovations in their recent work. “AI-Driven ‘Smart Laser Gun’ for Spatial Omics as an Innovative Biomarker Discovery Tool: From Lab Bench to Real-world.”
• Nitin Joshi, Brigham and Women’s Hospital—Harvard Medical School, has developed BraiN-TNGS, a high-throughput in vivo screening method using DNA barcodes to track and quantify nanoparticle (NP) formulations in the brain. This innovative approach enables the identification of NPs with high brain accumulation and precise cell-type targeting, optimizing therapeutic delivery while minimizing animal use and improving efficiency. “Unlocking Nano-Bio Interactions in the Brain for Precision Delivery of Gene Therapies.”
• Justin Langerman, UCLA, has developed a method called Secretion Encoded single-Cell sequencing (SEC-seq) using hydrogel particles (nanovials) to capture and analyze single cells and their secretions alongside transcriptomic data. This technique uncovered a unique population of mesenchymal stromal cells with high regenerative capacity, revealed insights into early cell-cell communication during embryonic development, and demonstrated the potential for investigating intricate cellular interactions. “Utilizing Nanovials to Associate Secretions and Transcriptomes of Single Cells with SEC-seq and to Capture the Discrete Effects of Cell-Cell Interaction”
• Maria Bueno Alvez Lim, KTH Royal Institute of Technology Stockholm, conducted a pilot pan-cancer study that compared cancer types against one another, analyzing 1,463 proteins from over 1,400 patients, uncovering a biomarker signature distinguishing specific cancers. Expanding this approach to 59 diseases, researchers created a Human Disease Blood Atlas, paving the way for more precise biomarker discovery and improved disease monitoring across various conditions. “Comprehensive Blood Proteome Profiling for Pan-Cancer and Pan-Disease Biomarker Discovery.”
• Babak Mahjour, MIT, whose study introduces an automated cheminformatic workflow that leverages expert-encoded reaction templates to systematically invent and optimize chemical reactions. Using a mechanistic network, the method predicts reaction pathways, demonstrated through both the regeneration of known multicomponent reactions and the discovery of novel transformations validated experimentally. The approach highlights its utility in exploring chemical space efficiently, with robotics-assisted experimentation providing insights into reaction kinetics and conditions. “Ideation and Evaluation of Novel Multicomponent Reactions via Mechanistic Network Analysis and Automation.”
• Caitlin Mills, Harvard Medical School, has developed a Dye Drop microscopy assay method for staining and fixing cells in culture with kinome-wide affinity data, allowing for the identification of drug targets in cancer and neurodegeneration, advancing cost-effective and robust drug development workflows. “Drug Response Phenotyping and Target Deconvolution Using Dye Drop Multiplexed Imaging.”
• Ritu Raman, MIT, who has found that current drug discovery often measures biomarkers instead of functional muscle force, leading to limited success in restoring mobility. The Raman Lab addresses this gap with engineered neuromuscular tissues and a patented flexure platform to quantify muscle function, enabling high-throughput screening for therapies aimed at improving muscle health and patient outcomes. “Tissue Engineering High-Throughput Models of the Neuromuscular System”